• Examination handed out via GitHub (Classroom): 7 May 2020
• Deadline to hand in results: 8 June 2020 (16:00)

• GitHub classroom group assignment.
• Basic starter code handed out as repository.
• A data analytics project based on a large data set, including the entire data pipeline.
• Tasks
  • Instructions in README
  • Improve efficiency of given code
  • Extend code: complete specific tasks
  • Explain/document procedure (conceptual understanding)
• ‘Product’: the repository, including R code, and a report in R markdown.

• Groups formed decentrally (same groups as for take-home exercises).
• Own research question, find a data set, think of approach/strategy, implement in R, presentation of results as Rmd/R-presentation recorded in a ‘screencast’.
• Hand in screencast via Canvas/Studynet (assignment is already open), commit code/rmd to GitHub-classroom (initial group formation assignment).

• By the end of the month, teams must be set!
• Please register, if you have not done so yet and join your team in GitHub Classroom!
• Still problems finding a team? Use the Q&A Section in Canvas! In case of emergencies, email me: ulrich.matter@unisg.ch

• RAM is not sufficient to handle the amount of data to be analyzed…
• What to do?
• Scale up by using parts of the available Mass Storage (hard-disk) as virtual memory

• Chunked data files on disk
• Memory-mapped files and shared memory

• Chunked data files on disk: ff-package
• Memory-mapped files and shared memory: bigmemory-package

• Data source: NYC Taxi & Limousine Commission (TLC)
• Data on all trip records including pick-up and drop-off times/locations.
  • (2009-2018)
  • Trip-level observations
  • Amount of fare paid
  • Amount of tip paid, etc.
• All raw data: over 200GB
  • Here: First 1 million observations (in January 2009)

#################################
# Fetch all TLC trip recrods
# Data source: 
# https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page
# Input: Monthly csv files from urls
# Output: one large csv file
# UM, St. Gallen, January 2019
#################################

# SET UP -----------------

# load packages
library(data.table)
library(rvest)
library(httr)

# fix vars
BASE_URL <- "https://s3.amazonaws.com/nyc-tlc/trip+data/yellow_tripdata_2018-01.csv"
OUTPUT_PATH <- "../data/tlc_trips.csv"
START_DATE <- as.Date("2009-01-01")
END_DATE <- as.Date("2018-06-01") # set to "2009-01-01" for the first file only


# BUILD URLS -----------

# parse base url
base_url <- gsub("2018-01.csv", "", BASE_URL)
# build urls
dates <- seq(from= START_DATE,
                   to = END_DATE,
                   by = "month")
year_months <- gsub("-01$", "", as.character(dates))
data_urls <- paste0(base_url, year_months, ".csv")

# FETCH AND STACK CSVS ----------------

# download, parse all files, write them to one csv
for (url in data_urls) {
     
     # download to temporary file
     tmpfile <- tempfile()
     download.file(url, destfile = tmpfile)
     
     # parse downloaded file, write to output csv, remove tempfile
     csv_parsed <- fread(tmpfile)
     fwrite(csv_parsed,
            file =  OUTPUT_PATH,
            append = TRUE)
     unlink(tmpfile)
     
}

• Background: Compute a statistic for specific groups (e.g. women vs men, etc.)

1. Split the data into subsamples (e.g. one for women, one for men)
2. Compute the statistic for each of the subsamples.
3. Combine all results in one table.

First, we read the raw taxi trips records into R with the ff-package.

# load packages
library(ff)
library(ffbase)

# set up the ff directory (for data file chunks)
if (!dir.exists("fftaxi")){
     system("mkdir fftaxi")
}
options(fftempdir = "fftaxi")

# import a few lines of the data, setting the column classes explicitly
col_classes <- c(V1 = "factor",
                 V2 = "POSIXct",
                 V3 = "POSIXct",
                 V4 = "integer",
                 V5 = "numeric",
                 V6 = "numeric",
                 V7 = "numeric",
                 V8 = "numeric",
                 V9 = "numeric",
                 V10 = "numeric",
                 V11 = "numeric",
                 V12 = "factor",
                 V13 = "numeric",
                 V14 = "numeric",
                 V15 = "factor",
                 V16 = "numeric",
                 V17 = "numeric",
                 V18 = "numeric")

# import the first one million observations
taxi <- read.table.ffdf(file = "../data/tlc_trips.csv",
                        sep = ",",
                        header = TRUE,
                        next.rows = 100000,
                        colClasses= col_classes,
                        nrows = 1000000
                        )

Following the data documentation provided by TLC, we give the columns of our data set more meaningful names.

# first, we remove the empty vars V8 and V9
taxi$V8 <- NULL
taxi$V9 <- NULL


# set covariate names according to the data dictionary
# see https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf
# note instead of taxizonne ids, long/lat are provided

varnames <- c("vendor_id",
              "pickup_time",
              "dropoff_time",
              "passenger_count",
              "trip_distance",
              "start_lat",
              "start_long",
              "dest_lat",
              "dest_long",
              "payment_type",
              "fare_amount",
              "extra",
              "mta_tax",
              "tip_amount",
              "tolls_amount",
              "total_amount")
names(taxi) <- varnames

# inspect the factor levels
levels(taxi$payment_type)

## [1] "Cash"      "CASH"      "Credit"    "CREDIT"    "Dispute"   "No Charge"

# recode them
levels(taxi$payment_type) <- tolower(levels(taxi$payment_type))
taxi$payment_type <- ff(taxi$payment_type,
                        levels = unique(levels(taxi$payment_type)),
                        ramclass = "factor")
# check result
levels(taxi$payment_type)

## [1] "cash"      "credit"    "dispute"   "no charge"

• Goal: a table that shows the average amount of tip paid for each payment-type category.
• Approach: ffdfply() and summaryBy()

# load packages
library(doBy)

# split-apply-combine procedure on data file chunks
tip_pcategory <- ffdfdply(taxi,
                          split = taxi$payment_type,
                          BATCHBYTES = 100000000,
                          FUN = function(x) {
                               summaryBy(tip_amount~payment_type,
                                         data = x,
                                         FUN = mean,
                                         na.rm = TRUE)})

## 2020-03-26 08:07:44, calculating split sizes

## 2020-03-26 08:07:44, building up split locations

## 2020-03-26 08:07:45, working on split 1/2, extracting data in RAM of 1 split elements, totalling, 0.0815 GB, while max specified data specified using BATCHBYTES is 0.09313 GB

## 2020-03-26 08:07:45, ... applying FUN to selected data

## 2020-03-26 08:07:45, ... appending result to the output ffdf

## 2020-03-26 08:07:45, working on split 2/2, extracting data in RAM of 3 split elements, totalling, 0.02281 GB, while max specified data specified using BATCHBYTES is 0.09313 GB

## 2020-03-26 08:07:45, ... applying FUN to selected data

## 2020-03-26 08:07:45, ... appending result to the output ffdf

Now we can have a look at the resulting summary statistic in the form of a data.frame().

as.data.frame(tip_pcategory)

##   payment_type tip_amount.mean
## 1         cash    0.0008161834
## 2       credit    2.1619737355
## 3      dispute    0.0035074627
## 4    no charge    0.0041056466

We add an additional variable percent_tip and then repeat the aggregation exercise for this variable.

# add additional column with the share of tip
taxi$percent_tip <- (taxi$tip_amount/taxi$total_amount)*100

# recompute the aggregate stats
tip_pcategory <- ffdfdply(taxi,
                          split = taxi$payment_type,
                          BATCHBYTES = 100000000,
                          FUN = function(x) {
                               summaryBy(percent_tip~payment_type, # note the difference here
                                         data = x,
                                         FUN = mean,
                                         na.rm = TRUE)})

## 2020-03-26 08:07:45, calculating split sizes

## 2020-03-26 08:07:45, building up split locations

## 2020-03-26 08:07:46, working on split 1/2, extracting data in RAM of 1 split elements, totalling, 0.08732 GB, while max specified data specified using BATCHBYTES is 0.09313 GB

## 2020-03-26 08:07:46, ... applying FUN to selected data

## 2020-03-26 08:07:46, ... appending result to the output ffdf

## 2020-03-26 08:07:46, working on split 2/2, extracting data in RAM of 3 split elements, totalling, 0.02444 GB, while max specified data specified using BATCHBYTES is 0.09313 GB

## 2020-03-26 08:07:46, ... applying FUN to selected data

## 2020-03-26 08:07:46, ... appending result to the output ffdf

# show result as data frame
as.data.frame(tip_pcategory)

##   payment_type percent_tip.mean
## 1         cash      0.005978433
## 2       credit     16.004172819
## 3      dispute      0.045659709
## 4    no charge      0.040432542

Goal: Get number of observations by covariate-values Approach: Cross-tabulatoni with table.ff() (ffbase-package)

table.ff(taxi$payment_type)

## 
##      cash    credit   dispute no charge 
##    781295    215424       536      2745

• What factors are correlated with payment types?
• Is payment type associated with the number of passengers in a trip?

# select the subset of observations only containing trips paid by credit card or cash
taxi_sub <- subset.ffdf(taxi, payment_type=="credit" | payment_type == "cash")
taxi_sub$payment_type <- ff(taxi_sub$payment_type,
                        levels = c("credit", "cash"),
                        ramclass = "factor")

# compute the cross tabulation
crosstab <- table.ff(taxi_sub$passenger_count,
                     taxi_sub$payment_type
                     )
# add names to the margins
names(dimnames(crosstab)) <- c("Passenger count", "Payment type")
# show result
crosstab

##                Payment type
## Passenger count credit   cash
##               0      2     44
##               1 149990 516828
##               2  32891 133468
##               3   7847  36439
##               4   2909  17901
##               5  20688  73027
##               6   1097   3588

# install.packages(vcd)
# load package for mosaic plot
library(vcd)

## Loading required package: grid

# generate a mosaic plot
mosaic(crosstab, shade = TRUE)

• Data still fit into RAM
• Possible with our subsample of 1 million rows (on most modern computers).
• Unlikely to work well with the full data set (200GB)

We use the already familiar fread() to import the same first million observations from the January 2009 taxi trips records.

# load packages
library(data.table)

# import data into RAM (needs around 200MB)
taxi <- fread("../data/tlc_trips.csv",
              nrows = 1000000)

We prepare/clean the data as in the ff-approach above.

# first, we remove the empty vars V8 and V9
taxi$V8 <- NULL
taxi$V9 <- NULL


# set covariate names according to the data dictionary
# see https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf
# note instead of taxizonne ids, long/lat are provided

varnames <- c("vendor_id",
              "pickup_time",
              "dropoff_time",
              "passenger_count",
              "trip_distance",
              "start_lat",
              "start_long",
              "dest_lat",
              "dest_long",
              "payment_type",
              "fare_amount",
              "extra",
              "mta_tax",
              "tip_amount",
              "tolls_amount",
              "total_amount")
names(taxi) <- varnames

# clean the factor levels
taxi$payment_type <- tolower(taxi$payment_type)
taxi$payment_type <- factor(taxi$payment_type, levels = unique(taxi$payment_type))     

• With [] syntax we index/subset usual data.frame objects in R.
• When working with data.tables, much more can be done in the step of ‘subsetting’ the frame.

taxi[, mean(tip_amount/total_amount)]

## [1] 0.03452489

And we can do the same with ‘splitting’ the rows first by specific groups and apply the function to each batch of observations.

taxi[, .(percent_tip = mean((tip_amount/total_amount)*100)), by = payment_type]

##    payment_type  percent_tip
## 1:         cash  0.005978433
## 2:       credit 16.004172819
## 3:    no charge  0.040432542
## 4:      dispute  0.045659709

Similarly we can use data.table’s dcast() for crosstabulation-like operations.

dcast(taxi[payment_type %in% c("credit", "cash")],
      passenger_count~payment_type, 
      fun.aggregate = length,
      value.var = "vendor_id")

##    passenger_count   cash credit
## 1:               0     44      2
## 2:               1 516828 149990
## 3:               2 133468  32891
## 4:               3  36439   7847
## 5:               4  17901   2909
## 6:               5  73027  20688
## 7:               6   3588   1097

• ‘Grammar of Graphics’
• Build plots layer-by-layer
• Here: Usefull tool for explorative visualization
• In-memory operations
  • Works well with 1 million obs.

Set up the canvas…

# load packages
library(ggplot2)

# set up the canvas
taxiplot <- ggplot(taxi, aes(y=tip_amount, x= fare_amount)) 
taxiplot

Visualize the co-distribution of the two variables with a simple scatter-plot.

# simple x/y plot
taxiplot +
     geom_point()

# simple x/y plot
taxiplot +
     geom_point(alpha=0.2)

Where are most observations located?

# 2-dimensional bins
taxiplot +
     geom_bin2d()

# 2-dimensional bins
taxiplot +
     stat_bin_2d(geom="point",
                 mapping= aes(size = log(..count..))) +
     guides(fill = FALSE)

# compute frequency of per tip amount and payment method
taxi[, n_same_tip:= .N, by= c("tip_amount", "payment_type")]
frequencies <- unique(taxi[payment_type %in% c("credit", "cash"),
                           c("n_same_tip", "tip_amount", "payment_type")][order(n_same_tip, decreasing = TRUE)])

# plot top 20 frequent tip amounts
fare <- ggplot(data = frequencies[1:20], aes(x = factor(tip_amount), y = n_same_tip)) 
fare + geom_bar(stat = "identity") 

fare + geom_bar(stat = "identity") + 
     facet_wrap("payment_type") 

Let’s have a closer look at non-zero tip amounts.

fare + geom_bar(data = frequencies[2:40],
                stat = "identity") + 
     facet_wrap("payment_type") 

Fractions of dollars due to loose change as tip?

# indicate natural numbers
taxi[, dollar_paid := ifelse(tip_amount == round(tip_amount,0), "Full", "Fraction"),]


# extended x/y plot
taxiplot +
     geom_point(alpha=0.2, aes(color=payment_type)) +
     facet_wrap("dollar_paid")

Rounding up?

taxi[, rounded_up := ifelse(fare_amount + tip_amount == round(fare_amount + tip_amount, 0),
                            "Rounded up",
                            "Not rounded")]
# extended x/y plot
taxiplot +
     geom_point(data= taxi[payment_type == "credit"],
                alpha=0.2, aes(color=rounded_up)) +
     facet_wrap("dollar_paid")

‘X% tip rule’?

modelplot <- ggplot(data= taxi[payment_type == "credit" & dollar_paid == "Fraction" & 0 < tip_amount],
                    aes(x = fare_amount, y = tip_amount))
modelplot +
     geom_point(alpha=0.2, colour="darkgreen") +
     geom_smooth(method = "lm", colour = "black")

## `geom_smooth()` using formula 'y ~ x'

modelplot <- ggplot(data= taxi[payment_type == "credit" & dollar_paid == "Fraction" & 0 < tip_amount],
                    aes(x = fare_amount, y = tip_amount))
modelplot +
     geom_point(alpha=0.2, colour="darkgreen") +
     geom_smooth(method = "lm", colour = "black") +
     ylab("Amount of tip paid (in USD)") +
     xlab("Amount of fare paid (in USD)") +
     theme_bw(base_size = 18, base_family = "serif")

## `geom_smooth()` using formula 'y ~ x'